Population dynamic models of microbial interactions

人类微生物群与健康之间的联系已经得到了大量的科学和流行的支持 请注意。通过允许快速且廉价地表征微生物群落组成，现代 测序发现了巨大的微生物多样性。确定微生物的存在与否 然而，这是不够的；我们需要了解功能失调的微生物群是如何形成的，以及如何修复它们。一个 朝着促进支持健康的微生物群落聚集的目标迈出的关键一步是预测 他们的时间动态。然而，仍然存在一个关键差距：理清因果关系和相关性。简单 如上所述，我们目前无法解释埋在极端中的生物学和临床相关性 微生物群落的复杂性。我们的长期目标是通过以下方式推进微生物组研究： 捕捉微生物相互作用中的因果关系的新模型；以及b)开发工具来解释 微生物相互作用对人类健康的影响。在开发数据分析管道之前，我们需要建立 这些方法所依据的理论基础。我们有三个目标专注于发展这样的 理论。(1)发展微生物相互作用的分子调节模型。现有的统计方法用于 微生物群的时间动力学建模建立在对微生物很少有效的假设之上 这可能会对社区造成严重误导。错误指定的模型可能会将研究人员误导到穷人 对动力学的预测，或者更糟糕的，对误导的治疗处方的预测，增强而不是抑制 感兴趣的微生物物种-如果感兴趣的物种是病原体，这是一个主要问题。我们将评估 在合成数据中考虑真实分子调节相互作用的统计时间序列模型的预测能力 并开发新的统计方法来解释时变的相互作用。(2)预测系统的稳定性 微生物组。即使很好地估计了控制微生物种群动态的相互作用，这些 相互作用可能与人类健康没有直接关系。相反，我们可能想要预测更高级别的属性 微生物的生命力，比如它的韧性。恢复力--微生物群维持和恢复功能的能力 在面对抗生素或机会性病原体等扰动时--是与数学有关的 稳定的概念。我们将制定新的措施来捕捉微生物组的弹性。(3)预测其他 高水平的微生物组特性。通常，微生物组的整体属性是有意义的，例如 调节pH值或代谢毒素的能力。借鉴种群遗传学理论，我们将开发新的 预测与微生物群有关的性状的时间动态的数学模型。加在一起，这些 AIMS将大大增强我们对微生物时间动力学的理解和解释 并为我们的能力奠定基础，使他们能够朝着预期的结果发展。 这项研究将为提高我们评估风险、设计合成微生物 社区执行任务，并操纵微生物群以促进健康。

The association between human microbiomes and health has garnered a great deal of scientific and popular attention. By allowing rapid and inexpensive characterization of microbial community composition, modern sequencing has uncovered enormous microbial diversity. Determining the presence versus absence of microbes is insufficient however; we need to understand how dysfunctional microbiomes form and how to repair them. A critical step toward the goal of promoting the assembly of microbial communities that support health is to predict their temporal dynamics. There remains, however, a critical gap: untangling causation from correlation. Simply stated, we are currently unable to interpret the biological and clinical relevance buried within the extreme complexity of microbial communities. Our long-term goal is to advance microbiome research by a) developing new models that capture causality in microbial interactions; and b) developing tools to interpret the relevance of microbial interactions for human health. Before the development of data analysis pipelines, we need to establish theoretical underpinnings upon which to base the methods. We have three aims focused on developing such theory. (1) Develop molecule-mediated models of microbial interactions. Existing statistical approaches for modeling the temporal dynamics of microbiomes are built on assumptions that are rarely valid for microbial communities and thus can be profoundly misleading. Misspecified models may mislead researchers toward poor prediction of dynamics, or worse, prescription of a misguided treatment that enhances rather than inhibits a microbial species of interest—a major problem if the species of interest is a pathogen. We will assess the predictive power of statistical time-series models given realistic molecule-mediated interactions in synthetic data and develop new statistical methods that account for time-varying interactions. (2) Predict stability of a microbiome. Even when interactions that govern microbial population dynamics are well estimated, these interactions may not be directly relevant to human health. Rather, we may want to predict higher-level properties of a microbiome such as its resilience. Resilience—the ability of a microbiome to maintain and recover function in the face of perturbations such as by antibiotics or opportunistic pathogens—is related to the mathematical concept of stability. We will develop new measures to capture the resilience of the microbiome. (3) Predict other high-level microbiome properties. Often a property of the microbiome in its entirety is of interest, such as the ability to regulate pH or metabolize a toxin. Borrowing from population genetic theory, we will develop novel mathematical models to predict the temporal dynamics of traits associated with the microbiome. Together, these aims will greatly enhance our understanding and interpretation of the temporal dynamics of microbial communities, and lay the foundation for our capacity to influence their trajectories toward desired outcomes. This research will provide a critical step in enhancing our ability to assess risk, design synthetic microbial communities to perform tasks, and manipulate microbiomes to promote health.